Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effe...Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.展开更多
The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO...The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.展开更多
The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are e...The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are evaluated.The grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing alloy.Note that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing alloy.The electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical activity.Using In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca alloying.The micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly improved.The In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.展开更多
Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid s...Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.展开更多
Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and c...Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.展开更多
Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting ac...Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R<sup>2</sup> values showed that Fuzzy-Γ<sub>1</sub>, Fuzzy-Γ<sub>2</sub>, Fuzzy-Γ<sub>3</sub>, Fuzzy-Γ<sub>4</sub> had good fitting performance, among which Fuzzy-Γ<sub>1</sub> had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ<sub>2</sub> distribution function was the second, and Fuzzy-Γ<sub>4</sub> distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ<sub>5</sub> is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ<sub>1</sub> and Fuzzy-Γ<sub>2</sub>.展开更多
As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path pla...As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.展开更多
Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked...Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.展开更多
This study aims to quantify the susceptibility of granular materials used in pavements to changes in moisture content and propose a correlation model to incorporate this susceptibility into seasonal analyses.The fines...This study aims to quantify the susceptibility of granular materials used in pavements to changes in moisture content and propose a correlation model to incorporate this susceptibility into seasonal analyses.The fines content and the percentage of fractured coarse aggregates were identified as direct indicators of the resilient modulus susceptibility to changes in water content.The results showed that the percentage of fractured coarse aggregates particles(FR)has a more significant impact on the resilient modulus(Er)of crushed granular materials used in pavement construction than the combined indicator of the fines content and sample volumetrics(nf).Crushed granular materials with a higher percentage of fractured coarse aggregates are relatively insensitive to changes in the degree of saturation,but become more sensitive as the fine fraction porosity decreases.An adjusted model was proposed based on the existing formulation,but considers a complex parameter to describe and adjust the sensitivity of base granular materials to variations in moisture content with respect to fabrication charac-teristics,fines content and volumetric properties.The model shows that the variation of Er values is below10%for fully crushed granular materials.However,it reaches approximately±12%for materials with 75%of crushed coarse aggregates andþ40%and-25%for materials with FR=50%.This model could help select good ag-gregates characteristics and adjust grain-size distribution for environments where significant moisture content variations can occur in the pavement system,such as in the Province of Quebec(Canada).As it is based on pa-rameters that can be easily determined or estimated,it also represents a valuable tool for detailed design and analysis that can consider material characteristics.展开更多
There are two broad objectives of the research reported in this paper. First, we assess whether government-provided cyber threat intelligence (CTI) is helpful in preventing, or responding to, cyber-attacks among small...There are two broad objectives of the research reported in this paper. First, we assess whether government-provided cyber threat intelligence (CTI) is helpful in preventing, or responding to, cyber-attacks among small businesses within the U.S. Defense Industrial Base (DIB). Second, we identify ways of improving the effectiveness of government-provided CTI to small businesses within the DIB. Based on a questionnaire-based survey, our findings suggest that government-provided CTI helps businesses within the DIB in preventing, or responding to, cyber-attacks providing a firm is familiar with the CTI. Unfortunately, a large percentage of small firms are not familiar with the government-provided CTI feeds and consequently are not utilizing the CTI. This latter situation is largely due to financial constraints confronting small businesses that prevent firms from having the wherewithal necessary to effectively utilize the government-provided CTI. However, we found a significant positive association between a firm’s familiarity with the government-provided CTI and whether a firm is being periodically reviewed by the Defense Counterintelligence and Security Agency (DCSA) or is compliant with the Cybersecurity Maturity Model Certification (CMMC) program. The findings from our study also show that the participating firms believe that external cyber threats are more likely to be the cause of a future cybersecurity breach than internal cybersecurity threats. Finally, our study found that the portion of the IT budget that small businesses within the DIB spend on cybersecurity-related activities is dependent on the perception that a firm would be the target of an external cyber-attack.展开更多
In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling struct...In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.展开更多
To optimize their Al_(2)O_(3)-SiO_(2) raw materials,anorthite based insulation refractories were prepared by the in-situ sintering process combined with the foaming method after sintering at 1350℃for 3 h,using green ...To optimize their Al_(2)O_(3)-SiO_(2) raw materials,anorthite based insulation refractories were prepared by the in-situ sintering process combined with the foaming method after sintering at 1350℃for 3 h,using green and pollution-free kaolin,kyanite,andalusite and sillimanite as Al_(2)O_(3)-SiO_(2) raw materials,respectively,and industrial CaCO_(3) as the CaO source.Effects of Al_(2)O_(3)-SiO_(2) raw material types on the physical properties,phase composition and microstructure were investigated.The results are as follows.All samples prepared by different Al_(2)O_(3)-SiO_(2) raw materials have hexagonal flake anorthite and a small amount of mullite and corundum.Their bulk density and thermal conductivity decrease in the order of using kaolin,andalusite,kyanite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,but their apparent porosity increases.Moreover,in the sample with kaolin,the bonding between anorthite crystals on the pore walls is closer than that of the other samples,which is conducive to increasing the cold crushing strength.The bonding between anorthite crystals on pore walls gradually decreases in the order of using kyanite,andalusite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,thus their cold crushing strength decreases accordingly.In comprehensive consideration,the properties of the sample from kyanite are the optimal.Its apparent porosity,thermal conductivity and cold crushing strength are 84.6%,0.141 W·m^(-1)·K^(-1) and 1.89 MPa,respectively.展开更多
Purpose research on nursing sensitive indicators in tumor Patients application effect in immune-related skin toxicity management. Method select our hospital April to June, 202360 cases patients with immune therapy set...Purpose research on nursing sensitive indicators in tumor Patients application effect in immune-related skin toxicity management. Method select our hospital April to June, 202360 cases patients with immune therapy settings as the control group. August-October, 2023 60 cases the patients treated with immune therapy were the experimental group. The control group adopted regular nursing methods, while the experimental group sensitive Indicators, evidence-based give preventive care. The social situation, psychological state, physical function, quality of life score, incidence of skin toxicity caused by immune checkpoint inhibitors, moderate and above of the two groups of patients were compared. Incidence of skin toxicity. Result: experience group SAS score, SDS score higher than the control group, the difference was statistically significant (P < 0.05);The incidence of skin toxic reactions caused by immune checkpoint inhibitors and the incidence of moderate and above skin toxic reactions in the experimental group are lower than those in the control group, and the difference is statistically significant (P < 0.05). Conclusion: sensitive indicator guidance evidence-based preventive care can reduce the degree of immune-related skin toxicity, improve the psychological state and quality of life of tumor patients treated with immune therapy and reduce the incidence of adverse reactions, improve nursing quality and patient satisfaction.展开更多
Objective: To explore the nursing effect of evidence-based PDCA cycle management mode in accelerated rehabilitation of patients undergoing thoracoscopic lung cancer radical surgery. Methods: 104 patients who underwent...Objective: To explore the nursing effect of evidence-based PDCA cycle management mode in accelerated rehabilitation of patients undergoing thoracoscopic lung cancer radical surgery. Methods: 104 patients who underwent thoracoscopic lung cancer radical surgery in our hospital from June 2022 to February 2023 were randomly divided into control group and intervention group, with 52 cases in each group. The control group implemented evidence-based ERAS clinical pathway management, while the intervention group implemented evidence-based PDCA cycle quality management. The postoperative recovery of the two groups of patients was compared. Results: The postoperative recovery of the intervention group was significantly better than that of the control group. The first time to get out of bed, the first time to eat, the duration of chest drainage tube placement, and the length of hospital stay were significantly shorter than those of the control group. The incidence of postoperative chest complications and hospitalization costs were significantly lower than those of the control group, and patient satisfaction was significantly higher than that of the control group (P Conclusion: Evidence-based PDCA cycle quality management mode can effectively improve the implementation quality of accelerated rehabilitation nursing for patients undergoing thoracoscopic lung cancer radical surgery, and it is worthy of clinical promotion.展开更多
An efficient and safe hydrogen storage method is one of the important links for the large-scale development of hydrogen in the future. Because of its low price and simple design, Ti-based hydrogen storage alloys are c...An efficient and safe hydrogen storage method is one of the important links for the large-scale development of hydrogen in the future. Because of its low price and simple design, Ti-based hydrogen storage alloys are considered to be suitable for practical applications. In this paper, we review the latest research on Ti-based hydrogen storage alloys. Firstly, the machine learning and density functional theory are introduced to provide theoretical guidance for the optimization of Ti-based hydrogen storage alloys. Then, in order to improve the hydrogen storage performance, we briefly introduce the research of AB type and AB2 type Ti-based alloys, focusing on doping elements and adaptive after treatment. Finally, suggestions for the future research and development of Ti-based hydrogen storage alloys are proposed. .展开更多
Objective: To explore the effect of evidence-based quality control circle (QCC) in improving the implementation rate of airway management measures in adult critically ill patients. Methods: Based on the Joanna Briggs ...Objective: To explore the effect of evidence-based quality control circle (QCC) in improving the implementation rate of airway management measures in adult critically ill patients. Methods: Based on the Joanna Briggs Institute (JBI) evidence-based health care model, the best evidence of airway management in adult critically ill patients was obtained and applied to the clinic. Results: The total implementation rate of airway management measures in adult critically ill patients increased from 23.62% before the implementation of quality control circle to 88.82%, and the pulmonary infection rate in critically ill patients decreased from 42.31% to 21.74%, with statistical significance between the two groups (P 0.05). Conclusion: Evidence-based quality control circle activities can standardize the practice standards of airway management in critically ill patients, reduce the occurrence of patients’ airway related complications, and improve clinical outcomes.展开更多
The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficultie...The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.展开更多
The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can i...The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.展开更多
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2020R1A2C1A01011131)the Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073164).
文摘Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.
基金financial support extended for this academic work by the Beijing Natural Science Foundation(Grant 2232066)the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant LSL-2212).
文摘The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.
基金supported by the National Natural Science Foundation of China(Grant Nos.:51901153)Shanxi Scholarship Council of China(Grant No.:2019032)+1 种基金Natural Science Foundation of Shanxi(Grant No.:202103021224049)the Science and Technology Major Project of Shanxi Province(Grant No.:20191102008,20191102007)。
文摘The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are evaluated.The grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing alloy.Note that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing alloy.The electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical activity.Using In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca alloying.The micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly improved.The In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.
基金supported by the National Natural Science Foundation of China(Grant Nos.51975227 and 12272144).
文摘Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.
基金supported by the Outstanding Youth Team Project of Central Universities(QNTD202308)the Ant Group through CCF-Ant Research Fund(CCF-AFSG 769498 RF20220214).
文摘Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.
文摘Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R<sup>2</sup> values showed that Fuzzy-Γ<sub>1</sub>, Fuzzy-Γ<sub>2</sub>, Fuzzy-Γ<sub>3</sub>, Fuzzy-Γ<sub>4</sub> had good fitting performance, among which Fuzzy-Γ<sub>1</sub> had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ<sub>2</sub> distribution function was the second, and Fuzzy-Γ<sub>4</sub> distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ<sub>5</sub> is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ<sub>1</sub> and Fuzzy-Γ<sub>2</sub>.
基金supported by National Natural Science Foundation of China(No.62073212)Shanghai Science and Technology Commission(No.23ZR1426600).
文摘As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.
文摘Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.
文摘This study aims to quantify the susceptibility of granular materials used in pavements to changes in moisture content and propose a correlation model to incorporate this susceptibility into seasonal analyses.The fines content and the percentage of fractured coarse aggregates were identified as direct indicators of the resilient modulus susceptibility to changes in water content.The results showed that the percentage of fractured coarse aggregates particles(FR)has a more significant impact on the resilient modulus(Er)of crushed granular materials used in pavement construction than the combined indicator of the fines content and sample volumetrics(nf).Crushed granular materials with a higher percentage of fractured coarse aggregates are relatively insensitive to changes in the degree of saturation,but become more sensitive as the fine fraction porosity decreases.An adjusted model was proposed based on the existing formulation,but considers a complex parameter to describe and adjust the sensitivity of base granular materials to variations in moisture content with respect to fabrication charac-teristics,fines content and volumetric properties.The model shows that the variation of Er values is below10%for fully crushed granular materials.However,it reaches approximately±12%for materials with 75%of crushed coarse aggregates andþ40%and-25%for materials with FR=50%.This model could help select good ag-gregates characteristics and adjust grain-size distribution for environments where significant moisture content variations can occur in the pavement system,such as in the Province of Quebec(Canada).As it is based on pa-rameters that can be easily determined or estimated,it also represents a valuable tool for detailed design and analysis that can consider material characteristics.
文摘There are two broad objectives of the research reported in this paper. First, we assess whether government-provided cyber threat intelligence (CTI) is helpful in preventing, or responding to, cyber-attacks among small businesses within the U.S. Defense Industrial Base (DIB). Second, we identify ways of improving the effectiveness of government-provided CTI to small businesses within the DIB. Based on a questionnaire-based survey, our findings suggest that government-provided CTI helps businesses within the DIB in preventing, or responding to, cyber-attacks providing a firm is familiar with the CTI. Unfortunately, a large percentage of small firms are not familiar with the government-provided CTI feeds and consequently are not utilizing the CTI. This latter situation is largely due to financial constraints confronting small businesses that prevent firms from having the wherewithal necessary to effectively utilize the government-provided CTI. However, we found a significant positive association between a firm’s familiarity with the government-provided CTI and whether a firm is being periodically reviewed by the Defense Counterintelligence and Security Agency (DCSA) or is compliant with the Cybersecurity Maturity Model Certification (CMMC) program. The findings from our study also show that the participating firms believe that external cyber threats are more likely to be the cause of a future cybersecurity breach than internal cybersecurity threats. Finally, our study found that the portion of the IT budget that small businesses within the DIB spend on cybersecurity-related activities is dependent on the perception that a firm would be the target of an external cyber-attack.
基金supported by the Natural Science Foundation of China underGrant 61833016 and 61873293the Shaanxi OutstandingYouth Science Foundation underGrant 2020JC-34the Shaanxi Science and Technology Innovation Team under Grant 2022TD-24.
文摘In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.
基金This work was supported by the National Natural Science Foundation of China(5180021223)Henan Provice Science&Technology Programs(232102231046 and 232102231051)Cultivation Programme for Yong Backbone Teachers in Henan University to Technology(2142121).
文摘To optimize their Al_(2)O_(3)-SiO_(2) raw materials,anorthite based insulation refractories were prepared by the in-situ sintering process combined with the foaming method after sintering at 1350℃for 3 h,using green and pollution-free kaolin,kyanite,andalusite and sillimanite as Al_(2)O_(3)-SiO_(2) raw materials,respectively,and industrial CaCO_(3) as the CaO source.Effects of Al_(2)O_(3)-SiO_(2) raw material types on the physical properties,phase composition and microstructure were investigated.The results are as follows.All samples prepared by different Al_(2)O_(3)-SiO_(2) raw materials have hexagonal flake anorthite and a small amount of mullite and corundum.Their bulk density and thermal conductivity decrease in the order of using kaolin,andalusite,kyanite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,but their apparent porosity increases.Moreover,in the sample with kaolin,the bonding between anorthite crystals on the pore walls is closer than that of the other samples,which is conducive to increasing the cold crushing strength.The bonding between anorthite crystals on pore walls gradually decreases in the order of using kyanite,andalusite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,thus their cold crushing strength decreases accordingly.In comprehensive consideration,the properties of the sample from kyanite are the optimal.Its apparent porosity,thermal conductivity and cold crushing strength are 84.6%,0.141 W·m^(-1)·K^(-1) and 1.89 MPa,respectively.
文摘Purpose research on nursing sensitive indicators in tumor Patients application effect in immune-related skin toxicity management. Method select our hospital April to June, 202360 cases patients with immune therapy settings as the control group. August-October, 2023 60 cases the patients treated with immune therapy were the experimental group. The control group adopted regular nursing methods, while the experimental group sensitive Indicators, evidence-based give preventive care. The social situation, psychological state, physical function, quality of life score, incidence of skin toxicity caused by immune checkpoint inhibitors, moderate and above of the two groups of patients were compared. Incidence of skin toxicity. Result: experience group SAS score, SDS score higher than the control group, the difference was statistically significant (P < 0.05);The incidence of skin toxic reactions caused by immune checkpoint inhibitors and the incidence of moderate and above skin toxic reactions in the experimental group are lower than those in the control group, and the difference is statistically significant (P < 0.05). Conclusion: sensitive indicator guidance evidence-based preventive care can reduce the degree of immune-related skin toxicity, improve the psychological state and quality of life of tumor patients treated with immune therapy and reduce the incidence of adverse reactions, improve nursing quality and patient satisfaction.
文摘Objective: To explore the nursing effect of evidence-based PDCA cycle management mode in accelerated rehabilitation of patients undergoing thoracoscopic lung cancer radical surgery. Methods: 104 patients who underwent thoracoscopic lung cancer radical surgery in our hospital from June 2022 to February 2023 were randomly divided into control group and intervention group, with 52 cases in each group. The control group implemented evidence-based ERAS clinical pathway management, while the intervention group implemented evidence-based PDCA cycle quality management. The postoperative recovery of the two groups of patients was compared. Results: The postoperative recovery of the intervention group was significantly better than that of the control group. The first time to get out of bed, the first time to eat, the duration of chest drainage tube placement, and the length of hospital stay were significantly shorter than those of the control group. The incidence of postoperative chest complications and hospitalization costs were significantly lower than those of the control group, and patient satisfaction was significantly higher than that of the control group (P Conclusion: Evidence-based PDCA cycle quality management mode can effectively improve the implementation quality of accelerated rehabilitation nursing for patients undergoing thoracoscopic lung cancer radical surgery, and it is worthy of clinical promotion.
文摘An efficient and safe hydrogen storage method is one of the important links for the large-scale development of hydrogen in the future. Because of its low price and simple design, Ti-based hydrogen storage alloys are considered to be suitable for practical applications. In this paper, we review the latest research on Ti-based hydrogen storage alloys. Firstly, the machine learning and density functional theory are introduced to provide theoretical guidance for the optimization of Ti-based hydrogen storage alloys. Then, in order to improve the hydrogen storage performance, we briefly introduce the research of AB type and AB2 type Ti-based alloys, focusing on doping elements and adaptive after treatment. Finally, suggestions for the future research and development of Ti-based hydrogen storage alloys are proposed. .
文摘Objective: To explore the effect of evidence-based quality control circle (QCC) in improving the implementation rate of airway management measures in adult critically ill patients. Methods: Based on the Joanna Briggs Institute (JBI) evidence-based health care model, the best evidence of airway management in adult critically ill patients was obtained and applied to the clinic. Results: The total implementation rate of airway management measures in adult critically ill patients increased from 23.62% before the implementation of quality control circle to 88.82%, and the pulmonary infection rate in critically ill patients decreased from 42.31% to 21.74%, with statistical significance between the two groups (P 0.05). Conclusion: Evidence-based quality control circle activities can standardize the practice standards of airway management in critically ill patients, reduce the occurrence of patients’ airway related complications, and improve clinical outcomes.
文摘The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.
基金This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.
文摘The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.
